A Path Recommendation Method Considering Individual Driving Preferences

The issue of congestion on urban roads stems from an imbalance between transport demand and supply. It has become imperative to address the problem traffic side. While managing effective relies understanding individual preferences drivers, current method for gathering (i.e., through questionnaires) is both expensive may not accurately capture characteristics respondents due their varying interpretations options. To overcome these challenges, we proposed a path recommendation that takes travel into consideration by employing automatic license plate recognition (ALPR) data extraction preferences. We initially identified key factors influencing selection behaviors including attributes, attributes. Subsequently, constructed satisfaction model based preferences, improved analytic hierarchy process (AHP). Furthermore, utilized pth percentile approach, rather than expert scores, in order determine relative importance each indicator AHP. By applying ALPR Xuancheng City, successfully extracted drivers. designed various scenarios verify reliability model, experimental results demonstrated can effectively influence underlying indicators behavior individuals with diverse considering different driver types Moreover, compared real trajectory, recommended paths yielded overall improvement over 10%, confirming practicality our model.